IJCI. International Journal of Computers and Information最新文献

{"title":"prediction of cardiovascular disease using machine learning techniques","authors":"S. Mohamed, M. Malhat, Gamal F. Elhady","doi":"10.21608/ijci.2022.129472.1071","DOIUrl":"https://doi.org/10.21608/ijci.2022.129472.1071","url":null,"abstract":"","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126861897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convergecast strategies for Wireless Body Area Networks environment : state of the art","authors":"A. Soliman, Hayam Mousa, Khalid Amin","doi":"10.21608/ijci.2022.109178.1065","DOIUrl":"https://doi.org/10.21608/ijci.2022.109178.1065","url":null,"abstract":"","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125085819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial (New front cover & Editorial board & Sample paper and Table of contents)","authors":"","doi":"10.21608/ijci.2022.211407","DOIUrl":"https://doi.org/10.21608/ijci.2022.211407","url":null,"abstract":"","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128052631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks","authors":"A. Ahmed, A. Keshk, Osama M. Abo-Seida, Mohamed Sakr","doi":"10.21608/ijci.2021.103605.1063","DOIUrl":"https://doi.org/10.21608/ijci.2021.103605.1063","url":null,"abstract":"","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115179707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heart Disease Classification Based on Hybrid Ensemble Stacking Technique","authors":"Ahmed El sheikh, Nader Mahmoud, A. Keshk","doi":"10.21608/ijci.2021.207732","DOIUrl":"https://doi.org/10.21608/ijci.2021.207732","url":null,"abstract":"Heart diseases are considered one of the leading death rates for humanity in the recent decades. The early diagnosis and prediction of heart disease becomes a critical subject in medical domain. Data mining techniques are usually used for finding anomalies, patterns and correlations within large data sets, thus it's crucial for clinical data analysis and various disease prediction. Ensemble approaches have proven to be quite effective in solving a variety of classification problems. In this research, we propose a hybrid ensemble stacking model with different feature engineering algorithms. The proposed ensemble model is based on five base models: Random Forest, Decision Tree, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Naïve Bayes for heart disease diagnosis. Logistic Regression meta model is used to merge base models predictions. We have examined various feature selection approaches such as: Brute Force, Principal Component Analysis (PCA), Classification and Regression Tree (CART) Feature Importance, and Logistic Regression based Recursive Feature Elimination. The proposed approach has been experimentally validated and evaluated on different dataset : UCI Cleveland and UCI Statlog. A quantitative evaluation shows that the combination of the ensemble model with brute force as feature selection technique yields a top accuracy of 97.8% for heart disease classification. the proposed stacking model has proven it's efficiency and overcomes existing approaches in heart diseases classification Keywords—Heart Disease; Data Mining; Classification; Ensemble Learning; Stacking; Feature Selection.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115361467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid approach for COVID-19 detection from chest radiography","authors":"E. Dawod, Nader Mahmoud, Ashraf B. Elsisi","doi":"10.21608/ijci.2021.207754","DOIUrl":"https://doi.org/10.21608/ijci.2021.207754","url":null,"abstract":"Automatic and rapid screening of COVID-19 from the chest X-ray and Computerized Tomography (CT) images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in both X-ray and CT images. Several models were introduced, but always there was a glitch that might be due to the use of a single classifier, and this reduces their accuracy. In this paper, we study the use of multi-classifiers and show their effect on different models working on X-ray and CT images. We perform a comparison study to show the high impact of ensemble stacking approach on top performer CNN models that recorded the highest detection accuracy in image detection and classification: COVID-Net, VGG16, ResNet, Bayesian, DenseNet, and DarkNet. We presented multi-classifiers instead of a single classifier stacked in an ensemble stacking approach for the diagnosis of the COVID19 from the Chest CT and Xray images. We provide a quantitative evaluation of the proposed ensemble stacking approach on two types of datasets: X-ray images and CT images datasets, with percentages reaching 99%. Keywords— COVID-19, stacked algorithm, ensemble technique, deep learning, chest X-ray images, Computerized Tomography (CT) images.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128775501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power-Aware Allocation of Virtual Machine-Based Real-Time Cloudlets in Cloud Data Centers","authors":"eman elbedewy, Anas A. Youssef, A. Keshk","doi":"10.21608/ijci.2021.207752","DOIUrl":"https://doi.org/10.21608/ijci.2021.207752","url":null,"abstract":"Due to the expanding utilization of cloud computing services, power consumption in cloud data centers has increased significantly. The number of active physical hosts impacts data center power usage, so the number of active physical hosts should be decreased. To achieve this goal, cloud data centers use virtualization technology to consolidate multiple virtual machines on a single physical server, using state-of-the-art virtual machine placement algorithms. Specifically, bin packing algorithms have been widely used to place a set of items, i.e., cloudlets and virtual machines, into a set of bins, i.e., virtual machines and physical hosts. However, a set of cloud services, i.e., cloudlets, are characterized as realtime and need to be provided within strict deadlines. In this paper, a cloud resource allocation framework is proposed to provide a compromise between two goals. The proposed framework uses the optimal physical host MIPS to achieve minimum possible power consumption while satisfying virtual machine-based cloudlets' deadline constraints. The proposed framework includes two modules, namely cloudlet allocator and virtual machine allocator. A set of widely used bin packing algorithms is exploited and compared in both modules. Firstly, the algorithms exploited in the cloudlet allocator module include first-fit, best-fit, and round-robin. The evaluation results showed that the round-robin algorithm provides the best outcomes in terms of real-time constraints. Round-robin could allocate an increasing number of cloudlets to virtual machines without scarifying the deadline constraints. Secondly, the algorithms used in the comparison in the virtual machine allocator module include first-fit, best-fit, next-fit, and worst-fit. The results showed that the best-fit algorithm reduces power consumption among all other algorithms under consideration. The results also suggest that setting the physical host CPU MIPS to optimal MIPS achieves the least consumed power. Keywords—Power-Aware Resource Allocation, Cloudlet Allocator, Virtual Machine Allocator, Bin Packing.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121612770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SBPAM: Secure Based Predictive Autoscaling Model For containerized application","authors":"M. Shenawy, Hayam Mousa, Khalid Amin","doi":"10.21608/ijci.2021.207823","DOIUrl":"https://doi.org/10.21608/ijci.2021.207823","url":null,"abstract":"","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"295 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132801695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security Testing of IOT Smart Applications","authors":"Shymaa Shaban, Eman M. Mohamed, A. Keshk","doi":"10.21608/ijci.2021.207747","DOIUrl":"https://doi.org/10.21608/ijci.2021.207747","url":null,"abstract":"-Internet of Things (IoT) offers a big variety of smart applications. Smart homes (SH) are a well-known utility of IoT that enhances quality of life. Security is the main challenge when discussing IoT due to the fact that it's far reachable across the world. Thus protecting SH against unauthorized customers may be very vital. Having access to sensor datasets is vital for SH research. Cost, time, low quality, and amount of present sensor datasets make it difficult for researchers to acquire data sets. SH simulation is a method to solve those problems. This paper proposes to enhance an existing SH simulation tool to be remotely managed inside a secure server. The data sets were created the usage of a hybrid, open-source SH simulator OpenSHS, (Open Smart Home Simulator), for generate data sets. The actions of persons in their life were collected by OpenSHS. The design in OpenSHS can only be managed via the Blender program. For remotely managed through objects in Blender, a web server was developed that automatic return some data on all objects in a designed home and manages far away their properties in a good manner. To upload protection for the server an internet web page turned into designed with password safety to save you unauthorized customers from having access to the server. At the end, the system protection was validated by different four passwords related on their success rate; the confusion matrix was used also for the high secure used password. The results reveal that the proposed method is actually do well prior to the SH research. Keywords--internet of things; smart applications; smart homes; simulation.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128896656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Static video summarization approach using Binary Robust Invariant Scalable Keypoints","authors":"Eman AboElenain, Khalid Amin, S. Zarif","doi":"10.21608/ijci.2021.207855","DOIUrl":"https://doi.org/10.21608/ijci.2021.207855","url":null,"abstract":"The constant demand and generation of digital video information have recently resulted in an increase in the growth of digital video content. Due to the rapid browsing of large amounts of data, content retrieval and indexing of video require an effective and advanced analysis technique. For quickly browsing, indexing, and accessing massive video archives, video summarizing approaches have been proposed. This research presents a new binary descriptor-based method for video summarization. The proposed method extracts key points and descriptors using a Binary Robust Invariant Scalable Key point (BRISK). For matching the binary descriptors between two successive frames, we employ a Brute-force method. And keyframes are extracted from each shot as the middle frame. Experiments were carried out using open video project data sets containing videos of various genres. The Comparison of user summaries (CUS) evaluation metric is used to assess the proposed method by calculating the accuracy and error rates and comparing it to other methods. As demonstrated by the experimental results, the proposed method gives good results when compared with other methods. Keywords— Video summarization, shot boundary detection, keyframe extraction, Binary Robust Invariant Scalable Keypoints (BRISK).","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131097077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}